Recently, a lot of image-processing technologies have been developed in order to obtain the image of superior quality in various backgrounds and environments, as the demand is growing for a high performance image processing equipment. However, an image sensor, the core of image processing equipment, has limitation of resolution and dynamic range. The dynamic range to which an image sensor reacts is narrower than one of actual input image recognized by human eyes. Thus, it causes the loss of image information. Consequently, that is the reason why the image seen by human eyes looks different from the image acquired by the image sensor. In addition, in case that there is dark lighting or backlight in the image, precise information may not be recognized.
In order to solve these problems, various studies for improving contrast are in progress. A representative method for improving contrast is a histogram equalization method, and there are various studies for methods modified from the histogram equalization. However, in case that the histogram of an input image is distributed intensively on the specific brightness value, over-enhancement and false contouring occur and contrast improvement enhancement is not performed for brightness value corresponding to histogram of less frequency or the relevant brightness value disappears. Thus, it is observed that detailed information of small area of images is lost.
In reference to technology for image improvement, Korean Patent Publication 10-2012-0060278 (hereinafter, ‘prior art’) and etc. were disclosed. The prior art above generates a histogram of an input image, adjusts image brightness by adapting histogram equalization to the input image based on cumulative distribution function of the histogram. But, in the prior art above, there is no description about the method in that compression ratio is determined by the characteristic of a histogram or a brightness value of the input image and the histogram is compressed.
Meanwhile, as a representative method for preventing excessive change of image brightness by over-enhancement, methods [1]-[7] for restraining excessive change of brightness by clipping a histogram of an input image and dispersing the histogram concentrated in specific brightness value were proposed.
SAPHE(Self-Adaptive Plateau Histogram Equalization)[1] and MSAPHE(Modified SAPHE)[2] chose clipping threshold as the median of local maxima of an input histogram, and modified the histogram by clipping exceeded part of the histogram, and performed equalization by using the modified histogram.
In BUBOHE(Histogram Equalization with Bin Underflow and Bin Overflow)[3], thresholds for upper limit, and lower limit are defined, and histogram equalization was performed by using a modified histogram after removing histogram which exceeds thresholds.
In WTHE(Weighted and Thresholded Histogram Equalization)[4], histogram which exceeds thresholds is removed like in BUBOHE, and the histogram equalization was performed after modifying the histogram by using normalized power law function for the histogram ranging between thresholds
In GC-CHE(Gain-Controllable Clipped Histogram Equalization)[5] a method was proposed that histogram removed by clipping process is redistributed to overall brightness area according to global gain, and modified histogram is made by redistributing additionally to bright area and dark area according to local gain.
In BHEPL (Bi-Histogram Equalization with a Plateau Limit)[6], in order to maintain an average brightness value of an input image, a histogram is divided into two brightness areas based on the average value of the input image, and then histogram equalization is performed by clipping independently for the respective area.
In QDHE (Quadrants Dynamic Histogram Equalization)[7], an input histogram is divided into quarters based on frequency of brightness of an input image, the range of an output brightness value of each area is determined according to the number of pixels contained in the area of each histogram, and then equalization is performed by using histogram which was clipping-processed independently for each area.
The histogram equalization methods based on clipping described above have the effect restraining occurrence of over-enhancement and false contouring. But the effect of contrast improvement is decreased and also unnatural images are acquired by failing to maintain the characteristic of an input image because the characteristic of the histogram is not considered and the same threshold is applied to the overall histogram.
[1] Bing-Jian Wang, Shang-Qian Liu, Qing Li, and Hui-Xin Zhou, “A real-time contrast enhancement algorithm for infrared images based on plateau histogram”, Infrared Physics & Technology, vol. 48, no. 1, pp. 77-82, April 2006.
[2] Nicholas Sia Pik Kong, Haidi Ibrahim, Chen Hee Ooi, and Derek Chan Juinn Chieh, “Enhancement of microscopic images using modified self-adaptive plateau histogram equalization”, submitted for publication in Proceedings of 2009 International Conference on Graphic and Image Processing (ICGIP 2009), Kota Kinabalu, Malaysia, November 2009.
[3] Seungjoon Yang, Jae Hwan Oh, and Yungfun Park, “Contrast enhancement using histogram equalization with bin underflow and bin overflow”, In Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on, vol. 1, pp. 881-884, September 2003.
[4] Qing Wang, and Rabab K. Ward, “Fast image/video contrast enhancement based on weighted thresholded histogram equalization”, IEEE Trans. Consumer Electronics, vol. 53, no. 2, pp. 757-764, May 2007
[5] Taekyung Kim and Joonki Paik, “Adaptive contrast enhancement using gain-controllable clipped histogram equalization”, IEEE Trans. on Consumer Electronics, vol. 54, no. 4, pp. 1803-1810, November 2008.
[6] Chen Hee Ooi, Sia Pik Kong, Haidi Ibrahim, “Bi-Histogram Equalization with a Plateau Limit for Digital Image Enhancement”, IEEE Transactions on Consumer Electronics, Vol. 55, No. 4, pp. 2072-2080, NOVEMBER 2009
[7] Chen Hee Ooi and Nor Ashidi Mat Isa, “Quadrants Dynamic Histogram Equalization for Contrast Enhancement”, IEEE Trans. Consumer Electronics, vol. 56, no. 4, pp. 2543-2551, May 2010